Since high school, when I built a simple chatbot to aid interventions for autistic individuals, I have been passionate about using computer science for social good. In college, my interest in biology and nutrition led me to explore, through machine learning and bioinformatics, the links between complex disorders and the human gut microbiome. Since 2021, I have continued this mission by applying democratizing tools like AutoML, enabling non-experts to leverage machine learning.

My current research focuses on alignment-free machine learning methods to analyze biological sequences that are too divergent for traditional annotation. By using language models to capture evolutionary and ecological signals in proteins, I aim to uncover novel enzymes that could advance medicine, industry, and sustainability. In simple terms, I am working to reveal the “functional dark matter” of proteins, the vast number whose roles are unknown because current comparison methods cannot detect their distant evolutionary links. Just as language models find patterns in human language, they can read the “language of life” to unlock nature’s untapped potential.